Search results

The current era is characterized by hyperturbulence, population growth, attention to food security, the need to identify sustainable strategies to reduce pollution and poverty, and the disparity between developed and undeveloped economies. These circumstances force a global paradigm shift based on sustainable practices and processes that put people and the environment at the core of each activity, contributing to sustainable, social, and economic development and promoting well-being in the community.

In this spirit, a strong impulse can derive from the practices of Green Technology, considered here as that set of processes aimed at eco-sustainability that acquire undisputed relevance, especially for emerging economies.

This chapter focuses on the role that Green technology practices exert in generating local well-being in the world's fifth-largest country: Brazil. Dynamic growth and effective social policies lifted millions of people out of poverty in the 2000s, even if socio-economic development varies widely across the country. Brazil is a leading global agricultural, minerals, and oil producer. The natural environment represents the primary source of Brazil's development that deserves to be protected and push firms and citizens to find new sustainable solutions based on green policies. Drawing inspiration from a Brazilian case study, this chapter proposes a set of building blocks that foster sustainable business practices in emerging countries.

The chapter is organized as follows: the first part introduces the concept of green technology practices; the second highlights the opportunities of green technologies; the third focuses on a single case study.

It is essential for developed nations to have adequate and functional infrastructure to sustain economic growth and well-being. Despite efforts to reduce the chances of infrastructure problems, several scholars have expressed concern about infrastructure standards deteriorating at an alarming rate and the need to ensure their sustainability. To achieve sustainable infrastructure development (SID), Sahely et al. (2005) proposed an uncomplicated strategy based on fundamental cooperation between infrastructure and ecological, economic, and social frameworks. Sustainable infrastructure is defined as the development and dependable management of a safe built environment based on efficient resource utilization and environmental standards. SID aims to mitigate or eliminate ecological problems and challenges while maximizing the potential social and economic benefits. United Nations Economic and Social Commission for Asia and the Pacific (UN-ESCAP, 2007) defines SID as infrastructure compatible with continued financial and environmental sustainability. Sustainable road development infrastructure (SRID) is a procedure for constructing infrastructure that incorporates all the essential sustainable development (SD) parameters and is complicated by the interdependence of multiple factors. Stakeholders are essential for the successful execution of infrastructure projects, and a comprehensive evaluation of stakeholder interests and requirements is necessary to achieve SRID goals while meeting the needs of all parties involved. To achieve SRID, it is necessary to understand the relationship between road infrastructure development (RID) processes and SD standards and to implement criteria and indicators that accurately depict the long-term viability of a development process. Al Sanad (2015) identified several factors that may impede the implementation of SD in the infrastructure sector. Shafii et al. (2006) identified insufficient knowledge of SID, lack of SID training, a perception of SID as expensive, acquisition concerns, administrative issues, expert capacities, and motivational factors for manufacturing local materials as potential obstacles. Serpell (2013) identified four categories of SID challenges, including knowledge, economic and financial, organizational, and go. Darko and Chan (2016) identified the most prevalent barriers as a lack of data, training, research, information, and expertise, high cost, government interest, premium and demand, and the absence of SID standards of practice. Azis et al. (2012) identified improved project efficiency, waste reduction in the construction industry, and energy efficiency as advantages of SID. Ametepey and Aigbavboa (2014) identified energy savings, environmental protection, contribution to a higher standard of living and a healthy work environment, resource preservation for future generations, reduction in lifecycle costs, promotion of sustainable economic development, and stakeholder satisfaction as the top benefits of sustainable construction (SC). Du Plessis (2007) identified technological factors, Al Sanad (2015) identified educational programmes, Serpell et al. (2013) identified transformation, economic, and stakeholder engagement as drivers of SC, and Hankinson and Breytenbach (2012) identified enhanced SC awareness. This chapter reviewed the literature on international infrastructure and sustainability development, discussing factors, and benefits promoting SID.

This chapter examines how information and communication technology (ICT) initiatives affect customers’ everyday routines with an emphasis on how electronic word of mouth (eWOM) affects their purchasing decisions and determines if eWOM might serve as a useful trust factor when making purchasing decisions. This chapter is based on both primary and secondary data and examines how eWOM affects customers’ buying decisions in this era of increasing social media platform usage. One hundred individuals from various regions of Karachi provided the primary data, collected through an online survey. Secondary data are also employed, such as details from business websites, scientific papers, and other related publications of businesses. In Karachi’s developing digital market, it was discovered that eWOM via social media used to have a substantial impact on consumers’ shopping habits. The effectiveness of social media content depends greatly on how appropriate it is for the intended audience. Social media marketing doesn’t quite simply aim to improve consumers’ impressions or directly advertise things. It also entails maintaining and fostering relationships between businesses and prospective customers. According to the research, consumers’ recommendations and ratings of goods and services made on social media, whether through eWOM or another channel, influenced their decisions to buy. Customers consider social media to be trustworthy when making decisions about what to buy. Customers are currently adopting social media as opposed to more conventional means to learn about new products. Most customers believe that eWOM from social media greatly affected their shopping choices, according to the results of the study.

The aim of this chapter is to provide an overview of the student mental health crisis in Higher Education (HE), and how resilience and grit, two important positive psychological constructs, can be beneficial for university students’ success and wellbeing. As part of a discussion around some of the current approaches to intervening in wellbeing in universities, the chapter provides evidence for the use of PPIs for wellbeing in university students, alongside some of the challenges of implementing these in HE. It also provides an overview of the Thriving Students Framework and presents a case for a multicomponent approach to monitoring and improving educational success. In particular, a wellbeing framework that, alongside resilience, also recognises the importance of strengths, persistence in the face of difficulty, a growth mindset, self-control and mental wellbeing; Academic Tenacity. The implications of utilising this framework for educational attainment in university students are discussed. The Bolton Uni-Stride Scale (BUSS), a single short measure of academic tenacity that combines the attributes enabling measurement and intervention to support university students to thrive, is also presented for educators to use.

Consumer behavior has evolved because of technological development. Nowadays, consumers carry out the different stages of the decision-making process by combining multiple devices which has been defined as multi, cross and omnichannel behavior. These behaviors have attracted the attention of academics and become a hot topic in literature. As a result, vast amounts of studies on the subject need to be revised and clarified. Thus, the aim of this chapter is to synthetize the primary academic literature that analyzes multi, cross and omnichannel behavior from the consumer point of view. To do that, first, the main concepts (multi, cross and omnichannel) and their differences are clarified. Second, the major findings of channel mix literature regarding the topics, channel scope and theories are exposed and described. Third, the opportunities and future lines of research are presented. This chapter contributes to the literature by clarifying the conceptualization of multi, cross and omnichannel behaviors; offering a complete picture of the main topics, channel approaches and theories addressed in channel mix literature; and presenting future research opportunities and open research questions in a channel mix context that could serve as a starting point to build further research.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.